JAX to PyTorch Converted Model (ResNet 10) It's done in context of porting HIL SERL paper code (https://hil serl.github.io/) to Lerobot (https://github.com/Lerobot/lerobot). The HF doesn't have ResNet 10 model, which could be pretty usefult for robotics tasks because of it's small size. This model is converted from JAX to PyTorch, and the weights are preserved. Model Description [Brief description of the original model and its purpose] This model is a PyTorch port of the original JAX implementation. The conversion maintains the original model's architecture and weights while making it accessible to PyTorch users. The original model is from https://github.com/rail berkeley/hil serl/blob/7d17d13560d85abffbd45facec17c4f9189c29c0/serl launcher/serl launcher/utils/train utils.py L103. Model Details Original Framework: JAX Target Framework: PyTorch Model Architecture: ResNet 10 (4 stage ResNet with basic blocks) Original Model: HIL SERL ResNet 10 Total Parameters: 4,905,792 (~4.9M parameters) Hidden Sizes: [64, 128, 256, 512] Input: 3 channel RGB images (128x128) Embedding Size: 64 Conversion Process This model was converted using an automated JAX to PyTorch conversion pipeline, ensuring…
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